Skill-Datei

Model Council

Name: Model Council
Author: openclaw

Multi-model consensus system — send a query to 3+ different LLMs via OpenRouter simultaneously, then a judge model evaluates all responses and produces a winner, reasoning, and synthesized best answer. Like having a board of AI advisors. Use for important decisions, code review, research verification.

openclaw4,189 Sterne15.02.2026

Beruf
Kategorien: Machine Learning

Skill-Inhalt

Get consensus from multiple AI models on any question.

Send your query to 3+ different LLMs simultaneously via OpenRouter. A judge model evaluates all responses and produces a winner, reasoning, and synthesized best answer.

When to Use

Important decisions — Don't trust one model's opinion
Code review — Get multiple perspectives on architecture choices
Research verification — Cross-check facts across models
Creative work — Compare writing styles and pick the best
Debugging — When one model is stuck, others might see the issue

How It Works

Your Question
    ├──→ Claude Sonnet 4    ──→ Response A
    ├──→ GPT-4o             ──→ Response B
    └──→ Gemini 2.0 Flash   ──→ Response C
                                    │
                              Judge (Opus) evaluates all
                                    │
                              ├── Winner + Reasoning
                              ├── Synthesized Best Answer
                              └── Cost Breakdown

Verwandte Skills

Model Council | Skills Pool

# Basic usage
python3 {baseDir}/scripts/model_council.py "What's the best database for a real-time analytics dashboard?"

# Custom models
python3 {baseDir}/scripts/model_council.py --models "anthropic/claude-sonnet-4,openai/gpt-4o,google/gemini-2.5-pro" "Your question"

# Custom judge
python3 {baseDir}/scripts/model_council.py --judge "openai/gpt-4o" "Your question"

# JSON output
python3 {baseDir}/scripts/model_council.py --json "Your question"

# Set max tokens per response
python3 {baseDir}/scripts/model_council.py --max-tokens 2000 "Your question"

Flag	Default	Description
`--models`	claude-sonnet-4, gpt-4o, gemini-2.0-flash	Comma-separated model list
`--judge`	anthropic/claude-opus-4-6	Judge model
`--max-tokens`	1024	Max tokens per council member
`--json`	false	Output as JSON
`--timeout`	60	Timeout per model (seconds)

═══ MODEL COUNCIL RESULTS ═══

Question: What's the best way to handle auth in a microservices architecture?

── Council Member Responses ──

🤖 anthropic/claude-sonnet-4 ($0.0043)
Use a centralized auth service with JWT tokens...

🤖 openai/gpt-4o ($0.0038)
Implement OAuth 2.0 with an API gateway...

🤖 google/gemini-2.0-flash-001 ($0.0012)
Consider using service mesh with mTLS...

── Judge Verdict (anthropic/claude-opus-4-6, $0.0125) ──

🏆 Winner: anthropic/claude-sonnet-4
Reasoning: Most comprehensive and practical approach...

📝 Synthesized Answer:
The best approach combines elements from all three...

💰 Total Cost: $0.0218

Model Council

When to Use

How It Works

Model Council

When to Use

How It Works

Quick Start

Configuration

Environment

Output Example

Credits

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns